TLDR: Goldman Sachs anticipates a significant surge in AI adoption starting in 2026, fueled by advancements in large language models (LLMs) leading to more accessible costs and a transformative impact on software development, enhancing productivity and expanding market size.
Goldman Sachs projects a substantial acceleration in the adoption of artificial intelligence (AI) from 2026 onwards, a trend expected to be propelled by the evolving economics of large language models (LLMs) and a deflationary effect on software development. George Lee, co-head of the Goldman Sachs Global Institute, highlighted that “2026, and beyond are the years where we’re really going to observe people being better, faster, smarter, more efficient, serving clients better and coming up with more ideas by leveraging these technologies.” This outlook suggests a shift from experimental AI use to widespread production, with early progress already showing an upward inflection in adoption metrics within Goldman Sachs itself.
The financial giant notes that AI advancements are currently outpacing even the optimistic forecasts from 2024, with a substantial increase in the usage of popular AI models. Investment in AI by major hyperscale technology companies is now projected to be approximately double what was initially predicted, signaling a robust commitment to the technology’s future. For instance, capex expectations for the four principal hyperscalers for 2026 have surged from about $207 billion to $405 billion.
A key driver of this acceleration is the anticipated impact of “Agentic AI” on the enterprise software ecosystem. Goldman Sachs predicts that this next stage of generative AI will fundamentally reshape the industry, with agents unleashing significant productivity benefits at the application layer over the next three years. By 2030, the global software market size is expected to expand by at least 20%, with the customer service software market potentially growing by 45%. Agents are projected to constitute over 60% of the entire software industry, becoming a new growth pole for enterprise productivity. While many current applications are still chatbot-based, more advanced AIs are supporting valuable use cases, and the ecosystem is actively addressing adoption barriers such as the lack of stable AI platforms and enterprise data strategies.
Furthermore, the commoditization of large language models is expected to play a crucial role. As LLMs become more accessible and their underlying “engines” become more standardized, the focus will shift, making data a significant competitive edge for companies. This trend, coupled with AI’s ability to enhance efficiency and automate tasks, contributes to a deflationary environment in software development, making AI solutions more cost-effective and attractive for broader enterprise adoption.
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Despite the rapid progress, questions remain regarding enterprise adoption, the return on investment for the substantial capital expenditure, and the emergence of definitive “killer apps.” However, the overall sentiment from Goldman Sachs points to an undeniable and accelerating integration of AI across industries, fundamentally transforming how businesses operate and innovate.
